Modality-Adaptive Transformer for Detecting Salient Objects from Arbitrary Modality Inputs

핵심 개념

A novel modality-adaptive Transformer (MAT) is proposed to effectively detect salient objects from inputs with arbitrary modalities by leveraging modality prompts to adaptively adjust the feature space and dynamically fusing unimodal features across different modalities.

초록

The paper presents a novel modality-adaptive Transformer (MAT) for the task of arbitrary modality salient object detection (AM SOD). AM SOD aims to detect salient objects from inputs with arbitrary modalities, such as RGB, depth, and thermal images.

The key components of MAT are:

1. Modality-Adaptive Feature Extractor (MAFE): MAFE takes an image of arbitrary modality along with a corresponding modality prompt as input. It uses the modality prompt to adaptively adjust the feature space to extract discriminative unimodal features. A novel modality translation contractive (MTC) loss is designed to learn modality-distinguishable prompts.

2. Channel-wise and Spatial-wise Fusion Hybrid (CSFH) Strategy: CSFH dynamically fuses the unimodal features from an arbitrary number of modalities. It employs a spatial-wise dynamic fusion module (SDFM) and a channel-wise dynamic fusion module (CDFM) to capture complementary detail and semantic information across modalities, respectively. CSFH aligns SDFM and CDFM to different levels of unimodal features based on their characteristics.

The proposed MAT can effectively handle the challenges of AM SOD, i.e., diverse modality discrepancies and dynamic fusion, and achieves significant performance improvements over existing models on the AM-XD benchmark dataset.

통계

The mean absolute error (M) quantifies the average absolute deviation between the predicted saliency map and the ground truth.
The mean F-measure (Fβ) offers a comprehensive measure of accuracy by harmonizing precision and recall.

인용구

"Specifically, inspired by prompt learning's ability of aligning the distributions of pre-trained models to the characteristic of downstream tasks by learning some prompts, MAT will first present a modality-adaptive feature extractor (MAFE) to tackle the diverse modality discrepancies by introducing a modality prompt for each modality."
"Accordingly, in the testing stage, MAFE can employ those learned modality prompts to adaptively adjust its feature space according to the characteristics of the input modalities, thus being able to extract discriminative unimodal features."
"Eventually, by virtue of MAFE, MTC loss and CSFH, our proposed MAT achieves significant increasements over existing models on those benchmark datasets."

핵심 통찰 요약

Modality Prompts for Arbitrary Modality Salient Object Detection

by Nianchang Hu... 게시일 arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03351.pdf

더 깊은 질문

How can the proposed modality prompts be further improved to better align the feature space with the characteristics of different modalities

The proposed modality prompts can be further improved by incorporating more sophisticated techniques to align the feature space with the characteristics of different modalities. One approach could be to introduce adaptive modality prompts that dynamically adjust based on the input data. This adaptability can be achieved by incorporating reinforcement learning techniques to update the modality prompts during the training process. By allowing the modality prompts to evolve based on the data distribution and task requirements, the model can better capture the unique features of each modality. Additionally, exploring the use of attention mechanisms within the modality prompts can help the model focus on relevant information for each modality, enhancing the discriminative power of the features extracted.

What other dynamic fusion strategies could be explored to better capture the complementary information across modalities

To better capture complementary information across modalities, alternative dynamic fusion strategies can be explored. One approach could be to integrate graph neural networks (GNNs) into the fusion process. GNNs are well-suited for capturing relationships and dependencies among different modalities in a graph structure. By representing the modalities as nodes in a graph and leveraging GNNs to propagate information across the graph, the model can effectively capture cross-modal interactions and extract more comprehensive features. Additionally, exploring attention mechanisms that dynamically adjust the fusion weights based on the content of each modality can enhance the model's ability to leverage complementary information.

How can the proposed MAT be extended to handle other computer vision tasks beyond salient object detection

The proposed MAT can be extended to handle other computer vision tasks beyond salient object detection by adapting the architecture and components to suit the specific requirements of the new tasks. For instance, for image segmentation tasks, the feature extraction module can be modified to extract features at different scales to capture both local and global information. The fusion strategy can be adjusted to combine features from multiple levels to improve segmentation accuracy. Additionally, for object recognition tasks, the decoder can be tailored to output class probabilities or bounding box coordinates instead of saliency maps. By customizing the components of the MAT model to the demands of the new tasks, it can be effectively applied to a wide range of computer vision applications.